sentiment-analysis

name	sentiment-analysis
description	Extract and quantify market sentiment signals from news, social media, and forums for a specific asset or sector.
metadata	{"marketbot":{"emoji":"🎭","triggers":["sentiment","crowd","social","forum"],"output":"sentiment-report","risk":"medium","freshness":"news-live","tools":["market_news","market_social_sentiment","browser_site"],"required_tools":["market_social_sentiment"],"markets":["a-share","hong-kong","us","global","mixed"],"asset_classes":["equity","crypto","commodity","etf"]}}

Sentiment Analysis

Use this skill to extract, quantify, and track the sentiment trends of a specific asset or the broader market from unstructured text data (news, social media, forums).

When to use

• User explicitly requests sentiment analysis for a ticker (e.g., /sentiment NVDA).
• Another skill (e.g., market-discovery, market-report, stock-watch) needs an objective sentiment score to form a conclusion.
• Tracking sentiment shifts over time (e.g., "Is the sentiment for AI chips improving?").

Data Sources & Weights

When conducting sentiment analysis, gather data from multiple sources and apply the following conceptual weights to form a final conclusion:

1. News (Weight: 0.5): Reuters, Bloomberg, CNBC. (High reliability).
2. Social Media (Weight: 0.3): X (Twitter), Stocktwits. (High noise, high speed, weight by engagement/likes/retweets).
3. Forums (Weight: 0.2): Reddit (e.g., r/wallstreetbets, r/investing). (Community driven, weight by upvotes).

Workflow

1. Data Gathering & Cleaning:

   • Use market news and social sentiment tools to gather raw text for the target asset/sector within the specified time window (e.g., 24h).
   • When site-native, logged-in, or dynamic discussion pages matter, supplement with browser_site on Xueqiu, Reddit, Zhihu, or similar platforms.
   • Clean the text by filtering out spam, ads, and irrelevant noise.
   • If the runtime exposes tools.market.sentimentBackend = finbert, prefer that backend for text scoring and mention the backend used in the result when it materially affects confidence.

2. Entity Extraction:

   • Identify the specific asset(s) and sectors mentioned in the text (e.g., "NVDA", "AI Chips").

3. Sentiment Classification & Scoring:

   • Analyze the text to classify it as positive, neutral, or negative.
   • Calculate an aggregated sentiment score for the asset (normalized to a scale, e.g., 0.0 to 1.0, where >0.5 is bullish).

4. Trend Calculation:

   • Compare current sentiment metric ($t$) against the previous period ($t-1$).
   • Determine the trend: Rising, Falling, or Stable.

5. Aggregation:

   • Synthesize the data to form a market, sector, or stock-level sentiment view.

Output Format

If returning directly to the user or generating a report, use the following format:

# 🎭 Sentiment Report: <ASSET/SECTOR>

## 📊 Overview
- **Sentiment Score**: <0.0 - 1.0> (<Bullish/Neutral/Bearish>)
- **Trend**: <Rising/Falling/Stable>

## 📰 Factors & Sources
- **News Sentiment**: <Score / View>
- **Social/Forum Sentiment**: <Score / View>

## 🔍 Key Drivers
- <Driver 1 (e.g., Strong AI GPU demand)>
- <Driver 2 (e.g., Positive analyst upgrades)>

## 📝 Conclusion
<One sentence actionable summary>

API / Tool Usage Example

When other skills or the system query this skill conceptually, it functions like an API returning:

{
  "asset": "NVDA",
  "window": "24h",
  "sentiment": 0.71,
  "trend": "rising",
  "sources": {
    "news": 0.76,
    "social": 0.65
  }
}

If queried via Natural Language, provide the structured Markdown report instead.